Peter Tinashe Mundowa

Data Scientist and AI Engineer · Harare, Zimbabwe

I turn undocumented enterprise data into measurable business value.

Data Scientist and AI Engineer at AI Creative Solutions in Harare. I build the data foundation an organisation runs on and the intelligence layer on top of it: ERP pipelines, lakes and warehouses, machine learning, and governed AI agents in production. My independent research works on the gap between models that excel in well-resourced conditions and models that hold up in the real world.

3× Microsoft CertifiedZindi Ambassador, ZimbabweFeatured in ITU NewsIndabaX Best Poster 2026Deep Learning Indaba 2027 (selected)
NowData Scientist and AI Engineer at AI Creative Solutions. Open to research collaboration, mentoring and speaking.
Recognition
University World NewsITU NewsIndabaX ZimbabweDeep Learning Indaba 2027 (selected)Zindi

Data science, put into production.

How my work fits togetherSources (ERP and accounting systems, legacy desktop databases and spreadsheets, satellite archives, AIS shipping data and field photos) feed a data layer (safe reads from live systems, batch and incremental ETL and ELT pipelines, lakes and warehouses with dimensional models, data quality and reconciliation), then models (machine learning and deep learning, statistics and anomaly detection, geospatial analysis, evaluation under real-world conditions), then delivery (governed AI agents, dashboards and management packs, production platforms, public dashboards), which serve decisions in operations, finance, sales, risk, site selection and crop monitoring. Each item links to the case study that shows it.SourcesERP and accountingsystems (SAP BusinessOne, Sage, aPostgreSQL-based ERP)Legacy desktopdatabases andspreadsheetsSatellite archivesAIS shipping dataField photosDataSafe reads from livesystemsBatch and incrementalpipelines (ETL and ELT)Lakes and warehouses,dimensional modelsData quality andreconciliationModelsMachine learning anddeep learningStatistics and anomalydetectionGeospatial analysisEvaluation underreal-world conditionsDeliveryGoverned AI agentsDashboards andmanagement packsProduction platformsPublic dashboardsDecisionsOperations, finance,sales and riskSite selectionCrop monitoringHow my work fits togetherSources (ERP and accounting systems, legacy desktop databases and spreadsheets, satellite archives, AIS shipping data and field photos) feed a data layer (safe reads from live systems, batch and incremental ETL and ELT pipelines, lakes and warehouses with dimensional models, data quality and reconciliation), then models (machine learning and deep learning, statistics and anomaly detection, geospatial analysis, evaluation under real-world conditions), then delivery (governed AI agents, dashboards and management packs, production platforms, public dashboards), which serve decisions in operations, finance, sales, risk, site selection and crop monitoring. Each item links to the case study that shows it.SourcesERP and accounting systems (SAP BusinessOne, Sage, a PostgreSQL-based ERP)Legacy desktop databases and spreadsheetsSatellite archivesAIS shipping dataField photosDataSafe reads from live systemsBatch and incremental pipelines (ETL andELT)Lakes and warehouses, dimensional modelsData quality and reconciliationModelsMachine learning and deep learningStatistics and anomaly detectionGeospatial analysisEvaluation under real-world conditionsDeliveryGoverned AI agentsDashboards and management packsProduction platformsPublic dashboardsDecisionsOperations, finance, sales and riskSite selectionCrop monitoring
Computer visionlive

BroilerVision: computer vision for broiler houses

An overhead camera, a detector and a dashboard for the stockperson: it counts broilers with an honest range and flags any bird that stops moving.

YOLO11nONNXPythonStreamlit
AI engineeringLive demo

Internal knowledge assistant: answers with sources, or an honest refusal

Answers staff questions only from the company's documents, cites the source behind every claim, and refuses when the documents cannot support an answer.

PythonFastAPIHybrid retrievalLlama 3.1Streamlit
Applied AI researchongoing

ZimCropGuard: multi-crop disease detection under domain shift

Ongoing research on multi-crop disease detection under real African field conditions. Best Poster, Deep Learning IndabaX Zimbabwe 2026.

Best Poster IndabaX 20265 crops 21+ classes54K+ training images
PyTorchEfficientNetB0ViT-B/16StreamlitPWA
AI engineering

AI agent orchestration

AI agents that answer questions on live business data within each user's permissions, with evidence for every figure and decisions left to people.

MindStudioClaudePostgreSQLTypeScriptReact
Data engineering

Live ERP integration and data pipelines

ERP and accounting data brought into lakes and warehouses without disturbing daily work, and checked before anyone relies on it.

SAP Business OneSagePostgreSQLT-SQLTypeScript
Machine learning researchLive demo

Tomato leaf disease detection: hybrid CNN-Transformer

My completed BSc Honours dissertation, deployed as a live Streamlit demo: a hybrid CNN-Transformer built to close the lab-to-field gap on budget phones.

+36pt low-light F1 gainLive Streamlit demoDeployed BSc Honours dissertation
PyTorchResNet18TransformerStreamlitscikit-learn
From my public data projectsDurban port calls, last 90 daysUpdated weekly · to 16 October 2026Durban daily port calls, last 90 days90 daily observations ending 16 October 2026; the last day had 9 port calls.9Durban recorded 9 port calls on 16 October 2026, across 90 days of data shown.

View all work →

Measured against the field.

I compete on Zindi as Zimbabwe's Ambassador, across computer vision, credit risk, LLM safety, policy proposals and tabular machine learning.

2
Gold medals
6
Silver medals
7
Bronze medals

15 medals across 41 challenges · best global rank 99 · from Zindi, November 2026

N counts ranked entries on the final leaderboard.

Full Zindi profile (PeterTheAnalyst) →

Verified, not just claimed.

Programmes

  • McKinsey Forward ProgramDec 2024
  • Aspire Leaders ProgramJan to Mar 2025
  • WorldQuant University Applied AI LabNov 2024 to May 2025
  • WorldQuant University Applied Data Science LabNov 2023 to Apr 2024
  • YALI Fundamentals of Responsible LeadershipAug 2024

Want to talk about this work? Email me or download my CV.